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Journal of Evaluation in Clinical Practice ISSN 1365-2753
PERSONAL VIEW
Evidence-based medical knowledge: the neglected role of expert opinion Jeannette Hofmeijer MD PhD1,2 1
Assistant Professor, Clinical Neurophysiology, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands; 2Neurologist, Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands
Introduction Evidence-based medicine (EBM) is listed as one of the 15 greatest medical milestones since 1840, along with the discovery of DNA structure, antibiotics, vaccines and viruses [1,2]. In a narrower sense, EBM refers to evidence-based medical practice, with cornerstones including that clinical decisions should be based on the best available scientific evidence and that the clinical problem (and not habits or protocols) should determine the type of evidence to be sought [3]. Evidence may be defined as anything supporting the truth of something else. To base assumptions upon evidence is a core feature of science and by its explicit appeal to evidence, EBM claims to be founded on the scientific method, where new knowledge results from experimental evidence, subjected to specific principles of reasoning [4]. From this scientific perspective, EBM is contrasted with the former paradigm of clinical medicine, in which medical knowledge was based on pathophysiological reasoning and clinical experience. At that time, experienced authority, manifested as the expert opinion, was assumed to be the main source of medical knowledge [5]. In EBM, proper (level 1) evidence is derived from empirical research on population samples, preferably randomized controlled trials. Evidence that is solely based on expert opinion is classified as weak and expert opinion ranks at the bottom of EBM’s hierarchy of medical knowledge, even below methodologically flawed clinical research (Table 1) [6]. This ranking indicates that empirical clinical studies are thought not to be tainted by an expert’s context or value judgements, and that conclusions from such studies are objectively valid, free of any prejudices or emotions. Because EBM’s preferred mode of investigation is hypothesis testing, it is tempting to define EBM’s scientific reasoning as a form of logical deduction [5], in which a logically certain conclu-
Table 1 Levels of evidence* 1 2 3 4 5
(Systematic review of) randomized controlled trials (Systematic review of) cohort studies or low-quality randomized controlled trials (Systematic review of) case-control studies Poor-quality cohort studies, case-control studies or case series Expert opinion
*According to the Oxford Centre of Evidence-Based Medicine (http:// www.cebm.net).
sion results from one or more general statements (premises) [7]. An important problem with designating EBM as such is that the premises of an argument in EBM are not axioma’s, but hypotheses of clinical experts, based on previous observation. Moreover, if various randomized controlled trials on the same treatment show conflicting results, and meta-analyses are not conclusive, consensus on the effect of the treatment under study has to be obtained from experts in the field. This indicates that although EBM is built on the presupposition that expert opinion is an invalid source of medical knowledge, its quest for evidence relies on it in both its first stage of hypothesis formulation and in its final stage of accepting the accumulated evidence as sufficient. Previous critical analyses concerning EBM have included the translation of evidence obtained from research on population samples to individual patients, for which each patient has to be reduced to a set of prognostic factors, and the ultimately pragmatic character of clinical trials, which can be used to test hypotheses regardless of any knowledge of basic mechanisms even when theories are merely speculative [8]. In this contribution, it is argued that the taunted expert opinion plays a large role in the pursuit of knowledge according to EBM standards. To illustrate that this polemic is an old one, the research tradition of EBM is first placed in the context of the history and philosophy of science. Subsequently, the different steps of pursuing knowledge according to EBM standards are analysed, making explicit the experts’ role in each step. Conclusions are illustrated with examples from neurological clinical practice. Examples are meant neither to undermine the scientific paradigm of EBM nor to argue for or against specific evidence, but to illustrate the role of expert opinion in its manifestation.
EBM as a revision of medical epistemology The term EBM implicitly claims that former clinical medicine, which was not based on meta-analyses or randomized controlled trials, was not based on evidence. However, the true change is a revision of medical epistemology with a conversion of the assumption of what can be known at all, in which way diseases and treatments should be studied, and how sound conclusions about optimal treatments should be obtained. For example, before the EBM era, the best evidence of effect of treatments was based on repeated but unsystematic observation of clinical experts, which is now assumed to be distorted by the expert’s viewpoint. EBM’s important gains include identification of such methodological weaknesses and solutions like randomization and blinding. From
Journal of Evaluation in Clinical Practice 20 (2014) 803–808 © 2014 John Wiley & Sons, Ltd.
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this perspective, a randomized clinical trial is merely an instrument to objectively measure effect sizes. The search for the optimal pursuit of knowledge is rather new within the field of medical reasoning. However, this subject has received centuries of attention by various philosophers of science [9,10]. In EBM, the revision of valid evidence is mainly driven by the aim to deprive it from subjectivity. Even stronger, objectivity is perceived as vital to proof superiority of one treatment over another, independently of any features of the researcher. To achieve this ideal, working medical scientists presume an objective reality, shared by all rational observers, which can be completely discovered by means of systematic observation and experimentation. The concept of such common reality is philosophically known as realism, with the opposite of idealism, implying that each subject generates its own reality. In science philosophy, the ideal of a value-free, objective, natural world has been toned down by many [9,11–13]. Analytical objections are that all scientific inquiries are primarily based on the researcher’s concepts, tools and thought patterns [12], whereas empirical objections include that researchers generally hang on to their own observations [9]. Since the Enlightenment, various models for valid scientific knowledge have been constructed. For centuries, knowledge was certainly believed to grow by an expert’s induction: accumulating observations leading to theory [14]. An example is the repeated finding of beta amyloid containing senile plaques and neurofibrillary tangles in the brains of patients with cognitive decline resulting from Alzheimer’s disease. This reiterating observation has resulted in the conclusion that beta amyloid plays an important role in the disease’s pathophysiology and that beta amyloid lowering therapies are of potential benefit for its treatment [15]. Popper rejected induction as a proper way to gain knowledge not only because of subjectivity but also because of logical invalidity: it prevents the proof of individual theories, which with a single observation can be shown to be false. He proposed falsifiability as the core feature of scientific theories and falsification as the empirical method to replace verifiability and induction [10]. As a response, Thomas Kuhn argued that subjective preferences assuredly play an important role, as individual researchers preferably hang onto their ‘scientific paradigms’, despite apparent falsification, and only unavoidable, substantial anomalies (‘crises’) will lead to ‘paradigm shift’ [9]. In Alzheimer’s disease, for example, many studies during the last three decades have shown the presence of amyloid beta aggregations in cognitively intact subjects, a poor correlation between cognitive functioning and level of dementia, and inability to affect disease progression by amyloid beta lowering therapies [16]. These findings suggest that amyloid beta accumulation is a downstream, end-stage manifestation of the disease rather than an early pathophysiological mechanism, of which slowing down could modify disease progression. Still, the majority of Alzheimer experts consider this evidence as insufficient to abandon the amyloid beta lowering paradigm as a hallmark of potentially effective treatment of the disease. Festinger’s concept of ‘cognitive dissonance’ may play a role here, indicating an expert’s discomfort caused by mentally coping with cognitions that conflict with the ones he adheres to. Such discomfort is mostly solved by adjusting the offending new cognition [17]. For example, Alzheimer experts usually hold that the lack of effect of amyloid beta lowering therapies cannot be 804
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attributed to the strategy of targeting amyloid beta, but to the timing of treatment [18]. A philosophical model that may be suitable to apply to EBM is Larry Lauden’s ‘research tradition’. This is defined as a set of rules about the appropriate methods to be used for investigation and construction of theories [13]. Research traditions have both general philosophical assumptions and specific theories about the nature of reality and ways in which it should be studied. In EBM, general assumptions include that reality is only approximately knowable, with inherent uncertainties, whereas a specific theory is that proper evidence is obtained by randomized controlled trials. Groups of patients are studied and the presumed reality is based on statistical probability. Knowledge is extrapolated from the general to the individual. Medical research traditions before the adoption of EBM designated expert opinion as a rational and valid source of knowledge. According to this tradition, the individual patient was the unit of observation and an objective, knowable reality was assumed. Consistently, basic characteristics of disease could be inferred from observation. Collecting knowledge on the course of diseases and effect of treatments depended on logical induction of accumulating observations. Knowledge was extrapolated from particular cases to the general, and general propositions resulted from specific examples [5]. Various traditions include different specific strengths and weaknesses. However, none are value free. Moreover, most theoretical frameworks, wherein scientific observations are made, are inflexible. This indicates that knowledge on the effectiveness of treatments remains contextual knowledge, which is strongly dependent on presuppositions about what questions are important, what sorts of connections are meaningful, and how these should be clarified [19].
Evidence according to EBM: the role of the expert In a narrower sense, EBM refers to methods for valid aggregation of knowledge, such as randomized controlled trials and metaanalyses. In a broader sense, it includes: (i) the identification of a medical problem and formulation of hypotheses concerning potentially beneficial diagnostics or treatments; (ii) study design and accomplishment; and (iii) interpretation of study results. In these three steps, expert opinion plays a non-negligible role.
Hypotheses are based on contextual knowledge Based on hypothesis testing, the reasoning involved in EBM is often referred to as logical deduction, in which one or more general statements (premises) lead to a logically certain conclusion. A purely rationalist argumentation allows deductive reasoning to be self-contained: propositions that are not self-evident can be unconditionally derived from others that are self-evident. However, deductive reasoning cannot readily be extrapolated to phenomena in the natural world, because this needs argumentation beyond rationally adopted premises influenced by observation. Therefore, empiricists who consider perceptions as the ultimate base of knowledge state that inductive inference (accumulating, unstandardized observation) precedes deductive argument and is necessary to create any associations or even causal relations [14].
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Figure 1 Tree diagram of the logical territory. An unsound deductive argument (argument with a false conclusion) can follow from a valid argumentation if one or more statements (premises) are false. Instead of being valid or invalid, inductive arguments and subsequent conclusions are either strong or weak.
In EBM, the indication of hypothesis testing differs from that in a logical philosophical argument: instead of true or untrue, conclusions include effect sizes of certain treatments with the corresponding accuracies. Still, hypothesis construction precedes argumentation. Hypothesis construction strongly depends on experts’ unstandardized observations. In this process, any statement is based on previous observation and the interpretation of current knowledge. As such, unstandardized observations by medical experts play a crucial role in the formulation of any medical scientific argument. In deductive reasoning, the evaluation of an argument involves two questions: (1) Are the premises true? And (2) is the argument valid? An argument is valid if the conclusion follows from the premises, notwithstanding the truth of the premises. This indicates that valid arguments can have false conclusions if premises are false (Fig. 1). Following the tradition of EBM, medical doctors and researchers are increasingly trained in biostatistics and medical epidemiology. This has led to an explosion of expertise in study design and interpretation in spite of shrinking knowledge on pathophysiological principles [20]. By this strong emphasis on study methodology, the validity of argument gets much attention. At the same time, with the devaluation of pathophysiological reasoning and experts’ observations as insufficient sources of knowledge, the importance of a medical argument’s premises or hypotheses is potentially undervalued. As an example, in patients with community-acquired bacterial meningitis, the underlying pathophysiology and accurate treatment of the severe complication of acute ischaemic stroke are unknown [21]. Adequate prevention or treatment of infarcts may reduce the high rates of morbidity and case fatality [22]. Speculations and research are done by neurologists exposed to neurological infectious diseases (meningitis) and by other neurologists specialized in cerebrovascular diseases (ischaemic stroke). Among the first group, there is a widely held belief that cerebral infarction after bacterial meningitis is most likely caused by (para) infectious inflammatory conditions, such as vasculitis [22,23]. Accordingly, research is directed at the detection of signs of inflammation [22]. Otherwise, neurologists mainly exposed to ischaemic stroke propose intravascular coagulation as the major cause [24]. Their analyses are directed at the detection of intravascular coagulation and showed fibrin thrombi, but not inflammatory vessel wall infiltrates, in patients with bacterial meningitis and brain infarcts [24]. Neurologists exposed to infectious diseases report on treatment
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with corticosteroids and projected future research includes trials to estimate the effect of suppression of inflammation [22]. Otherwise, stroke neurologists, based on their observations, suggest that prevention of the formation of fibrin is likely beneficial and propose trials on drugs inhibiting the coagulation cascade [24]. The example stresses the influence of hypothesis construction on the way towards new knowledge on treatments. Popper has stated that the process of growing knowledge resembles Darwin’s natural selection, by which he indicated a ‘natural selection of hypotheses’ [25]. During this selection, a medical hypothesis’ adoption and spread are determined not only by its inherent fitness but also by the level by which it is valued, and whether it is subjected to empirical research to be accepted. Hypotheses, either true or false, are strongly influenced by the experts’ context. Therefore, although hypothesis construction may be considered to precede instead of being part of EBM’s reasoning, any potential new evidence on the best treatment of patients still depends on experts’ previous observations, inferences and beliefs.
Trial design is influenced by personal preferences In medical practice, preferences on whether it is desirable to maximize the probability of a particular clinical outcome depend on value judgements of individual experts. In moral philosophy, value statements can never follow directly from statements of facts [26]. This indicates that empirical data cannot tell what ought to be done when conclusions are value based. Evidence from clinical trials provides information to create hypothetical imperatives, such as ‘if you want to maximize the probability of outcome A, then do B’. However, whether or not the probability of outcome A should be maximized is not an empirical question and unanswerable by an appeal to evidence. As in the choice of treatments in medical practice, value judgements play an important role in the production of evidence as aspired by medical research. For example, balancing benefits and harms of potential treatment outcomes is affected by social or personal preferences. Consequently, these influence the choice of outcome measures and definitions of clinically relevant effect sizes [27]. In other words, the appreciation of a specific outcome as ‘good’ or ‘poor’ influences trial design, implying that personal value judgements affect the probability of a trial outcome to be positive or negative. Surgical decompression for space-occupying hemispheric infarction may serve as an example. If treated medically, patients with space-occupying hemispheric infarction have a poor prognosis with case fatality rates of approximately 80% [28]. Surgical decompression, through removal of a large part of the skull followed by a duraplasty, can normalize intracranial pressure and accommodate shifts of brain tissue, thereby preventing secondary damage or transtentorial herniation. In the 1990s, observational studies with historical controls have shown that surgical decompression reduces the high death rates with more than 50% [29,30]. Thereafter, this treatment has been advocated by experts with a strong belief in its benefit, despite the lack of evidence from randomized controlled trials. However, others were sceptical, as the large reduction in mortality by the operation probably came at the expense of an increase of severely disabled survivors, where they valued survival with neurological impairment leading to com805
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plete dependency lower than death. In the 2000s, three randomized clinical trials have been performed, testing the hypothesis that surgical decompression reduces mortality and improves functional outcome of patients with space-occupying hemispheric infarction. Two trials were designed by believers in the treatment’s benefit [31,32] whereas one was designed by sceptics [33]. In all the trials, the primary outcome measure was defined as death or disability as determined by the score on the modified Rankin Scale (mRS). Grades on the mRS range from 0 (no symptoms at all) to 5 (severe disability). Outcome was dichotomized as ‘favourable’ or ‘unfavourable’. The believing investigators of the first two trials defined a score of 0–4 as a favourable outcome. The sceptical investigators of the third trial valued a score of 4, indicating moderately severe disability and the inability to walk or attend to own bodily needs without assistance, as an unfavourable outcome. Through the classification of survival with severely impaired functional outcome as ‘good’, and the high prior probability of a reduction of mortality, the chance of finding positive trial results was higher for the first two trials as compared with the third. Results were accordingly: although distributions of outcomes were largely similar over the three trials, the first two concluded that surgical decompression increases the probability of a good functional outcome of patients with space-occupying hemispheric infarction [31,32], whereas the third showed no clinically relevant beneficial treatment effect [33]. This example illustrates that experts’ preferences or value judgements may have a critical impact on trial design and consequently on outcome. Because such value judgements are mostly left implicit, these influences are often hardly recognizable.
Interpretation of trial results depends on prior beliefs In EBM as a research tradition, reality is entirely phenomenological. Statements on the probability of occurrence of the various phenomena, for example, ‘relapsing stroke’, are based on arithmetic probability. For the inherent uncertainties, there is a frequent use of Bayesian statistics. Herewith, quantifications of uncertainties are obtained in the form of probability statements: a posterior probability is derived as a function of prior probability and the new data [34]. For example, in patients with ischaemic stroke and patent foramen ovale (PFO), a lower incidence of poor outcome in patients treated with oral anticoagulants as compared with those treated with platelet aggregation inhibitors in a subgroup analysis of a large randomized controlled trial has resulted in shifts of the assumed risk-benefit ratio of this treatment, with higher posterior than prior probabilities of benefit [35]. Another example is the updated meta-analysis on the effect of a combination of aspirin and dipyridamole as compared with aspirin alone for secondary prevention of vascular events after ischaemic stroke. Until 2006, meta-analyses of previous trials were inconclusive. Adding the results of the large, multicentre, randomized ESPRIT trial provided sufficient evidence to prefer the combination regimen [36]. In other words, Bayesian statistics are helpful in rationally redressing beliefs if new evidence is presented. Objective Bayesians seek an objective value for the prior probability. However, most hold that the prior probabilities represent subjective probabilities, also called degrees of belief. These degrees of belief are a function of the scientist’s belief in the proposition under research [37]. In medical sciences, subjective (prior) prob806
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abilities can be based on experts’ previous experiences: repeated observations in the past increase the subjective probability that a next observation will be likewise. As such, prior probabilities rather reflect how likely the scientist thinks a proposition is true than how likely this truth actually is. Thus, the use of prior probabilities implicitly justifies an expert’s inductive inferences. For example, in patients with cryptogenic ischaemic stroke and PFO, there is controversy about the usefulness of PFO closure to prevent stroke relapses [38]. Simplistically stated, cardiologists have a stronger belief in the treatment’s benefit than neurologists. If any, evidence for a causal relation between PFO and stroke comes from observational studies, mostly published in neurological journals, with various methodological weaknesses. For instance, in patients with cryptogenic stroke and PFO, other potential causes were often insufficiently excluded, and PFO was present not only in patients with brain infarcts but also in approximately 20% of healthy control subjects [39]. Therefore, neurologists are sceptical about cerebral infarcts being caused by PFO and consequently about any potential benefit of PFO closure. Otherwise, in cardiologic journals, a large number of observational studies have been published, suggesting that the rate of relapsing stroke was reduced after PFO closure [35]. Despite lack of control groups and unclear criteria for relapsing stroke in the majority, these studies have engendered cardiologists’ general belief in the benefit of PFO closure. If priors are indeed subjective, there is no objective standard for choosing between such conflicting hypotheses. Hence, Bayesian statistics can be used to rationally justify belief in any hypothesis, but this implies a rejection of objectivism. In 2012 and 2013, the benefit of PFO closure has been studied in three randomized controlled trials [40–42]. In all the three trials, there was a discretely lower incidence of poor outcome after PFO closure as compared with no closure. However, in none of the trials, this difference was statistically significant. These results have confirmed the relatively strong prior belief in the benefit of the treatment in the community of cardiologists, who claim that the lack of statistical significance is caused by heterogeneity of the patient groups and relatively short follow-up [43]. Otherwise, the same data, together with the low priors among neurologists, have led to even stronger neurological recommendations against PFO closure [44,45]. Accepting accumulated evidence as sufficient does not follow simple deductive rules. Rather, each step of the process, determining which evidence to include, defining the strength of the evidence and assessing generalizability, requires interpretive judgements [20]. There is no solution for conflicting results. If at all, only repeated application of Bayes’ theorem can lead to agreement on posterior probability.
Conclusion Expert opinion plays an important role in all essential steps of the pursuit of knowledge in the research tradition of EBM. It may be inferred that evidence from expert opinion differs in kind instead of in degree from evidence from randomized controlled trials. If so, these do not belong to the same quality ranking and should be defined as complementary rather than hierarchically ranked. Acknowledgement of prior beliefs and preferences may enhance interpretation and extrapolation of trial results and strengthen conclusions.
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Conflict of interest The author declares no conflict of interest.
Acknowledgements The author thanks Dr M. Boenink (philosopher), Professor Dr A. Algra (clinical epidemiologist) and Professor Dr ir. M.J.A.M. van Putten (neurologist) for critically reading and commenting for previous versions of the manuscript. Funding sources played no role in its preparation or in the decision to submit.
References 1. Guyatt, G. (1996) Evidence based medicine. Annals of Internal Medicine, 114 (Suppl. 2), A–16. 2. Sackett, D. L., Rosenberg, W. M., Gray, J. A., Haynes, R. B. & Richardson, W. S. (1996) Evidence based medicine: what it is and what it isn’t. BMJ (Clinical Research Ed.), 312, 71–72. 3. Davidoff, F., Haynes, B., Sackett, D. & Smith, R. (1995) Evidence based medicine. BMJ (Clinical Research Ed.), 310, 1085–1086. 4. Kuhn, T. S. (1961) The function of measurement in modern physical science. Isis; an International Review Devoted to the History of Science and Its Cultural Influences, 52, 161–193. 5. Kulkarni, A. V. (2005) The challenges of evidence-based medicine: a philosophical perspective. Medicine, Health Care, and Philosophy, 8, 255–260. 6. Evidence-Based Medicine Working Group (1992) Evidence-based medicine. A new approach to teaching the practice of medicine. JAMA: The Journal of the American Medical Association, 268, 2420– 2425. 7. Grayling, A. C. (1997) An Introduction to Philosophical Logic. Oxford: Blackwell publishing Ltd. 8. Wolff, H. & Gotzsche, P. (2000) Medicine and the Humanities. In Rational Diagnosis and Treatment, 3rd edn (eds H. Wulff & P. Gotzsche), pp. 147–174. Oxford: Blackwell Science. 9. Kuhn, T. S. (1970) The Structure of Scientific Revolutions, 3rd edn. London: University of Chicago press. 10. Popper, K. R. (1992) The Logic of Scientific Discovery. London: Routledge. 11. Aristotle (1920) Ethica Nicomachea. Oxford: Oxford University Press. 12. Popper, K. R. (2002) Conjectures and Refutations: The Growth of Scientific Knowledge. London: Routledge. 13. Batts, B. & Crawford, L. L. (1991) Problematic progress: a review of Laudan’s Progress and its problems and science and values. Journal of the Experimental Analysis of Behavior, 55, 337–349. 14. Kyriacou, D. N. (2004) Evidence-based medical decision making: deductive versus inductive logical thinking. Academic Emergency Medicine: Official Journal of the Society for Academic Emergency Medicine, 11, 670–671. 15. Anderton, B. H. (1987) Alzheimer’s disease. Progress in molecular pathology. Nature, 325, 658–659. 16. Castellani, R. J., Lee, H. G., Siedlak, S. L., Nunomura, A., Hayashi, T. & Nakamura, M. (2009) Reexamining Alzheimer’s disease: evidence for a protective role for amyloid-beta protein precursor and amyloidbeta. Journal of Alzheimer’s Disease, 18, 447–452. 17. Festinger, L., Riecken, W. & Schachter, S. (2008) When prophecy fails. London: Pinter & Martin Ltd. 18. Callaway, E. (2012) Alzheimer’s drugs take a new tack. Nature, 489, 13–14. 19. Longino, H. E. (1990) Science as Social Knowledge: Value and Objectivity in Scientific Inquiry. Princeton: Princeton University Press.
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Personal View
20. Feinstein, A. R. & Horwitz, R. I. (1997) Problems in the ‘evidence’ of ‘evidence-based medicine’. The American Journal of Medicine, 103, 529–535. 21. Schut, E. S., Brouwer, M. C., de Gans, J., Florquin, S., Troost, D. & van de Beek, D. (2009) Delayed cerebral thrombosis after initial good recovery from pneumococcal meningitis. Neurology, 73, 1988–1995. 22. Schut, E. S., Lucas, M. J., Brouwer, M. C., Vergouwen, M. D., van der Ende, A. & van de Beek, D. (2012) Cerebral infarction in adults with bacterial meningitis. Neurocritical Care, 16, 421–427. 23. Klein, M., Koedel, U., Kastenbauer, S., Pfister, H. W., van de Beek, D. & Scut, E. S. (2010) Delayed cerebral thrombosis after initial good recovery from pneumococcal meningitis: past as prologue: delayed stroke as a parainfectious process of bacterial meningitis? Neurology, 75, 193. 24. Vergouwen, M. D., Schut, E. S., Troost, D. & van de Beek, D. (2010) Diffuse cerebral intravascular coagulation and cerebral infarction in pneumococcal meningitis. Neurocritical Care, 13, 217–227. 25. Simonton, D. K. (1999) Darwinian Genius: The Future of An Idea. Origins of Genius Darwinian Perspectives on Creativity. Oxford: Oxford University Press. 26. Miller, C. (2009) The conditions of moral realism. Journal of Philosophical Research, 34, 123–155. 27. Strech, D. & Tilburt, J. (2008) Value judgments in the analysis and synthesis of evidence. Journal of Clinical Epidemiology, 61, 521–524. 28. Hacke, W., Schwab, S., Horn, M., Spranger, M., De Georgia, M. & von Kummer, R. (1996) ‘Malignant’ middle cerebral artery territory infarction: clinical course and prognostic signs. Archives of Neurology, 53, 309–315. 29. Rieke, K., Schwab, S. & Krieger, D. (1995) Decompressive surgery in space-occupying hemispheric infarction: results of an open, prospective trial. Critical Care Medicine, 23, 1576–1587. 30. Schwab, S., Steiner, T. & Aschoff, A. (1998) Early hemicraniectomy in patients with complete middle cerebral artery infarction. Stroke; a Journal of Cerebral Circulation, 29, 1888–1893. 31. Vahedi, K., Vicaut, E., Mateo, J., Kurtz, A., Orabi, M. & Guichard, J. P. (2007) Sequential-design, multicenter, randomized, controlled trial of early decompressive craniectomy in malignant middle cerebral artery infarction (DECIMAL Trial). Stroke; a Journal of Cerebral Circulation, 38, 2506–2517. 32. Juttler, E., Schwab, S., Schmiedek, P., Unterberg, A., Hennerici, M. & Woitzik, J. (2007) Decompressive Surgery for the Treatment of Malignant Infarction of the Middle Cerebral Artery (DESTINY): a randomized, controlled trial. Stroke; a Journal of Cerebral Circulation, 38, 2518–2525. 33. Hofmeijer, J., Kappelle, L. J., Algra, A., Amelink, G. J., van Gijn, J. & van der Worp, H. B. (2009) Surgical decompression for spaceoccupying cerebral infarction (the Hemicraniectomy After Middle cerebral artery infarction with Life-threatening Edema Trial [HAMLET]): a multicentre, open, randomised trial. Lancet Neurology, 8, 326–333. 34. Gurrin, L. C., Kurinczuk, J. J. & Burton, P. R. (2000) Bayesian statistics in medical research: an intuitive alternative to conventional data analysis. Journal of Evaluation in Clinical Practice, 6, 193–204. 35. Kitsios, G. D., Dahabreh, I. J., Abu Dabrh, A. M., Thaler, D. E. & Kent, D. M. (2012) Patent foramen ovale closure and medical treatments for secondary stroke prevention: a systematic review of observational and randomized evidence. Stroke; a Journal of Cerebral Circulation, 43, 422–431. 36. ESPRIT Study Group (2006) Aspirin plus dipyridamole versus aspirin alone after cerebral ischaemia of arterial origin (ESPRIT): randomised controlled trial. Lancet, 367, 1665–1673. 37. Jaynes, E. T. (1986) Annals of Internal Medicine, 114 (Suppl. 2), A–16. 38. Homma, S. & Di Tullio, M. R. (2010) Patent foramen ovale and stroke. Journal of Cardiology, 56, 134–141.
807
Personal View
39. Sacco, R. L., Ellenberg, J. H., Mohr, J. P., Tatemichi, T. K., Hier, D. B. & Price, T. R. (1989) Infarcts of undetermined cause: the NINCDS Stroke Data Bank. Annals of Neurology, 25, 382–390. 40. Carroll, J. D., Saver, J. L., Thaler, D. E., Smalling, R. W., Berry, S. & Mac Donald, L. A. (2013) Closure of patent foramen ovale versus medical therapy after cryptogenic stroke. The New England Journal of Medicine, 368, 1092–1100. 41. Furlan, A. J., Reisman, M., Massaro, J., Mauri, L., Adams, H. & Albers, G. W. (2012) Closure or medical therapy for cryptogenic stroke with patent foramen ovale. The New England Journal of Medicine, 366, 991–999. 42. Meier, B., Kalesan, B., Mattle, H. P., Khattab, A. A., Hildick-Smith, D. & Dudek, D. (2013) Percutaneous closure of patent foramen ovale in cryptogenic embolism. The New England Journal of Medicine, 368, 1083–1091.
808
J. Hofmeijer
43. Hakeem, A., Marmagkiolis, K., Hacioglu, Y., Uretsky, B. F., Gundogdu, B. & Leesar, M. (2013) Safety and efficacy of device closure for patent foramen ovale for secondary prevention of neurological events: Comprehensive systematic review and meta-analysis of randomized controlled trials. Cardiovascular Revascularization Medicine: Including Molecular Interventions, 14, 349–355. 44. Hofmeijer, J., Lindeboom, J. E. & Vermeer, S. E. (2014) Persistent foramen ovale and cerebral ischemia: treatment options? Tijdschrift voor Neurologie en Neurochirurgie, 114, 105–110. 45. Kappelle, L. J. (2013) Closure of patent foramen ovale after a TIA or minor ischaemic stroke; yet another logical, but unnecessary treatment. Nederlands Tijdschrift Voor Geneeskunde, 157, A6818.
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Journal of Evaluation in Clinical Practice ISSN 1365-2753
PERSONAL VIEW
Evidence-based medical knowledge: the neglected role of expert opinion Jeannette Hofmeijer MD PhD1,2 1
Assistant Professor, Clinical Neurophysiology, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands; 2Neurologist, Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands
Introduction Evidence-based medicine (EBM) is listed as one of the 15 greatest medical milestones since 1840, along with the discovery of DNA structure, antibiotics, vaccines and viruses [1,2]. In a narrower sense, EBM refers to evidence-based medical practice, with cornerstones including that clinical decisions should be based on the best available scientific evidence and that the clinical problem (and not habits or protocols) should determine the type of evidence to be sought [3]. Evidence may be defined as anything supporting the truth of something else. To base assumptions upon evidence is a core feature of science and by its explicit appeal to evidence, EBM claims to be founded on the scientific method, where new knowledge results from experimental evidence, subjected to specific principles of reasoning [4]. From this scientific perspective, EBM is contrasted with the former paradigm of clinical medicine, in which medical knowledge was based on pathophysiological reasoning and clinical experience. At that time, experienced authority, manifested as the expert opinion, was assumed to be the main source of medical knowledge [5]. In EBM, proper (level 1) evidence is derived from empirical research on population samples, preferably randomized controlled trials. Evidence that is solely based on expert opinion is classified as weak and expert opinion ranks at the bottom of EBM’s hierarchy of medical knowledge, even below methodologically flawed clinical research (Table 1) [6]. This ranking indicates that empirical clinical studies are thought not to be tainted by an expert’s context or value judgements, and that conclusions from such studies are objectively valid, free of any prejudices or emotions. Because EBM’s preferred mode of investigation is hypothesis testing, it is tempting to define EBM’s scientific reasoning as a form of logical deduction [5], in which a logically certain conclu-
Table 1 Levels of evidence* 1 2 3 4 5
(Systematic review of) randomized controlled trials (Systematic review of) cohort studies or low-quality randomized controlled trials (Systematic review of) case-control studies Poor-quality cohort studies, case-control studies or case series Expert opinion
*According to the Oxford Centre of Evidence-Based Medicine (http:// www.cebm.net).
sion results from one or more general statements (premises) [7]. An important problem with designating EBM as such is that the premises of an argument in EBM are not axioma’s, but hypotheses of clinical experts, based on previous observation. Moreover, if various randomized controlled trials on the same treatment show conflicting results, and meta-analyses are not conclusive, consensus on the effect of the treatment under study has to be obtained from experts in the field. This indicates that although EBM is built on the presupposition that expert opinion is an invalid source of medical knowledge, its quest for evidence relies on it in both its first stage of hypothesis formulation and in its final stage of accepting the accumulated evidence as sufficient. Previous critical analyses concerning EBM have included the translation of evidence obtained from research on population samples to individual patients, for which each patient has to be reduced to a set of prognostic factors, and the ultimately pragmatic character of clinical trials, which can be used to test hypotheses regardless of any knowledge of basic mechanisms even when theories are merely speculative [8]. In this contribution, it is argued that the taunted expert opinion plays a large role in the pursuit of knowledge according to EBM standards. To illustrate that this polemic is an old one, the research tradition of EBM is first placed in the context of the history and philosophy of science. Subsequently, the different steps of pursuing knowledge according to EBM standards are analysed, making explicit the experts’ role in each step. Conclusions are illustrated with examples from neurological clinical practice. Examples are meant neither to undermine the scientific paradigm of EBM nor to argue for or against specific evidence, but to illustrate the role of expert opinion in its manifestation.
EBM as a revision of medical epistemology The term EBM implicitly claims that former clinical medicine, which was not based on meta-analyses or randomized controlled trials, was not based on evidence. However, the true change is a revision of medical epistemology with a conversion of the assumption of what can be known at all, in which way diseases and treatments should be studied, and how sound conclusions about optimal treatments should be obtained. For example, before the EBM era, the best evidence of effect of treatments was based on repeated but unsystematic observation of clinical experts, which is now assumed to be distorted by the expert’s viewpoint. EBM’s important gains include identification of such methodological weaknesses and solutions like randomization and blinding. From
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this perspective, a randomized clinical trial is merely an instrument to objectively measure effect sizes. The search for the optimal pursuit of knowledge is rather new within the field of medical reasoning. However, this subject has received centuries of attention by various philosophers of science [9,10]. In EBM, the revision of valid evidence is mainly driven by the aim to deprive it from subjectivity. Even stronger, objectivity is perceived as vital to proof superiority of one treatment over another, independently of any features of the researcher. To achieve this ideal, working medical scientists presume an objective reality, shared by all rational observers, which can be completely discovered by means of systematic observation and experimentation. The concept of such common reality is philosophically known as realism, with the opposite of idealism, implying that each subject generates its own reality. In science philosophy, the ideal of a value-free, objective, natural world has been toned down by many [9,11–13]. Analytical objections are that all scientific inquiries are primarily based on the researcher’s concepts, tools and thought patterns [12], whereas empirical objections include that researchers generally hang on to their own observations [9]. Since the Enlightenment, various models for valid scientific knowledge have been constructed. For centuries, knowledge was certainly believed to grow by an expert’s induction: accumulating observations leading to theory [14]. An example is the repeated finding of beta amyloid containing senile plaques and neurofibrillary tangles in the brains of patients with cognitive decline resulting from Alzheimer’s disease. This reiterating observation has resulted in the conclusion that beta amyloid plays an important role in the disease’s pathophysiology and that beta amyloid lowering therapies are of potential benefit for its treatment [15]. Popper rejected induction as a proper way to gain knowledge not only because of subjectivity but also because of logical invalidity: it prevents the proof of individual theories, which with a single observation can be shown to be false. He proposed falsifiability as the core feature of scientific theories and falsification as the empirical method to replace verifiability and induction [10]. As a response, Thomas Kuhn argued that subjective preferences assuredly play an important role, as individual researchers preferably hang onto their ‘scientific paradigms’, despite apparent falsification, and only unavoidable, substantial anomalies (‘crises’) will lead to ‘paradigm shift’ [9]. In Alzheimer’s disease, for example, many studies during the last three decades have shown the presence of amyloid beta aggregations in cognitively intact subjects, a poor correlation between cognitive functioning and level of dementia, and inability to affect disease progression by amyloid beta lowering therapies [16]. These findings suggest that amyloid beta accumulation is a downstream, end-stage manifestation of the disease rather than an early pathophysiological mechanism, of which slowing down could modify disease progression. Still, the majority of Alzheimer experts consider this evidence as insufficient to abandon the amyloid beta lowering paradigm as a hallmark of potentially effective treatment of the disease. Festinger’s concept of ‘cognitive dissonance’ may play a role here, indicating an expert’s discomfort caused by mentally coping with cognitions that conflict with the ones he adheres to. Such discomfort is mostly solved by adjusting the offending new cognition [17]. For example, Alzheimer experts usually hold that the lack of effect of amyloid beta lowering therapies cannot be 804
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attributed to the strategy of targeting amyloid beta, but to the timing of treatment [18]. A philosophical model that may be suitable to apply to EBM is Larry Lauden’s ‘research tradition’. This is defined as a set of rules about the appropriate methods to be used for investigation and construction of theories [13]. Research traditions have both general philosophical assumptions and specific theories about the nature of reality and ways in which it should be studied. In EBM, general assumptions include that reality is only approximately knowable, with inherent uncertainties, whereas a specific theory is that proper evidence is obtained by randomized controlled trials. Groups of patients are studied and the presumed reality is based on statistical probability. Knowledge is extrapolated from the general to the individual. Medical research traditions before the adoption of EBM designated expert opinion as a rational and valid source of knowledge. According to this tradition, the individual patient was the unit of observation and an objective, knowable reality was assumed. Consistently, basic characteristics of disease could be inferred from observation. Collecting knowledge on the course of diseases and effect of treatments depended on logical induction of accumulating observations. Knowledge was extrapolated from particular cases to the general, and general propositions resulted from specific examples [5]. Various traditions include different specific strengths and weaknesses. However, none are value free. Moreover, most theoretical frameworks, wherein scientific observations are made, are inflexible. This indicates that knowledge on the effectiveness of treatments remains contextual knowledge, which is strongly dependent on presuppositions about what questions are important, what sorts of connections are meaningful, and how these should be clarified [19].
Evidence according to EBM: the role of the expert In a narrower sense, EBM refers to methods for valid aggregation of knowledge, such as randomized controlled trials and metaanalyses. In a broader sense, it includes: (i) the identification of a medical problem and formulation of hypotheses concerning potentially beneficial diagnostics or treatments; (ii) study design and accomplishment; and (iii) interpretation of study results. In these three steps, expert opinion plays a non-negligible role.
Hypotheses are based on contextual knowledge Based on hypothesis testing, the reasoning involved in EBM is often referred to as logical deduction, in which one or more general statements (premises) lead to a logically certain conclusion. A purely rationalist argumentation allows deductive reasoning to be self-contained: propositions that are not self-evident can be unconditionally derived from others that are self-evident. However, deductive reasoning cannot readily be extrapolated to phenomena in the natural world, because this needs argumentation beyond rationally adopted premises influenced by observation. Therefore, empiricists who consider perceptions as the ultimate base of knowledge state that inductive inference (accumulating, unstandardized observation) precedes deductive argument and is necessary to create any associations or even causal relations [14].
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Figure 1 Tree diagram of the logical territory. An unsound deductive argument (argument with a false conclusion) can follow from a valid argumentation if one or more statements (premises) are false. Instead of being valid or invalid, inductive arguments and subsequent conclusions are either strong or weak.
In EBM, the indication of hypothesis testing differs from that in a logical philosophical argument: instead of true or untrue, conclusions include effect sizes of certain treatments with the corresponding accuracies. Still, hypothesis construction precedes argumentation. Hypothesis construction strongly depends on experts’ unstandardized observations. In this process, any statement is based on previous observation and the interpretation of current knowledge. As such, unstandardized observations by medical experts play a crucial role in the formulation of any medical scientific argument. In deductive reasoning, the evaluation of an argument involves two questions: (1) Are the premises true? And (2) is the argument valid? An argument is valid if the conclusion follows from the premises, notwithstanding the truth of the premises. This indicates that valid arguments can have false conclusions if premises are false (Fig. 1). Following the tradition of EBM, medical doctors and researchers are increasingly trained in biostatistics and medical epidemiology. This has led to an explosion of expertise in study design and interpretation in spite of shrinking knowledge on pathophysiological principles [20]. By this strong emphasis on study methodology, the validity of argument gets much attention. At the same time, with the devaluation of pathophysiological reasoning and experts’ observations as insufficient sources of knowledge, the importance of a medical argument’s premises or hypotheses is potentially undervalued. As an example, in patients with community-acquired bacterial meningitis, the underlying pathophysiology and accurate treatment of the severe complication of acute ischaemic stroke are unknown [21]. Adequate prevention or treatment of infarcts may reduce the high rates of morbidity and case fatality [22]. Speculations and research are done by neurologists exposed to neurological infectious diseases (meningitis) and by other neurologists specialized in cerebrovascular diseases (ischaemic stroke). Among the first group, there is a widely held belief that cerebral infarction after bacterial meningitis is most likely caused by (para) infectious inflammatory conditions, such as vasculitis [22,23]. Accordingly, research is directed at the detection of signs of inflammation [22]. Otherwise, neurologists mainly exposed to ischaemic stroke propose intravascular coagulation as the major cause [24]. Their analyses are directed at the detection of intravascular coagulation and showed fibrin thrombi, but not inflammatory vessel wall infiltrates, in patients with bacterial meningitis and brain infarcts [24]. Neurologists exposed to infectious diseases report on treatment
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with corticosteroids and projected future research includes trials to estimate the effect of suppression of inflammation [22]. Otherwise, stroke neurologists, based on their observations, suggest that prevention of the formation of fibrin is likely beneficial and propose trials on drugs inhibiting the coagulation cascade [24]. The example stresses the influence of hypothesis construction on the way towards new knowledge on treatments. Popper has stated that the process of growing knowledge resembles Darwin’s natural selection, by which he indicated a ‘natural selection of hypotheses’ [25]. During this selection, a medical hypothesis’ adoption and spread are determined not only by its inherent fitness but also by the level by which it is valued, and whether it is subjected to empirical research to be accepted. Hypotheses, either true or false, are strongly influenced by the experts’ context. Therefore, although hypothesis construction may be considered to precede instead of being part of EBM’s reasoning, any potential new evidence on the best treatment of patients still depends on experts’ previous observations, inferences and beliefs.
Trial design is influenced by personal preferences In medical practice, preferences on whether it is desirable to maximize the probability of a particular clinical outcome depend on value judgements of individual experts. In moral philosophy, value statements can never follow directly from statements of facts [26]. This indicates that empirical data cannot tell what ought to be done when conclusions are value based. Evidence from clinical trials provides information to create hypothetical imperatives, such as ‘if you want to maximize the probability of outcome A, then do B’. However, whether or not the probability of outcome A should be maximized is not an empirical question and unanswerable by an appeal to evidence. As in the choice of treatments in medical practice, value judgements play an important role in the production of evidence as aspired by medical research. For example, balancing benefits and harms of potential treatment outcomes is affected by social or personal preferences. Consequently, these influence the choice of outcome measures and definitions of clinically relevant effect sizes [27]. In other words, the appreciation of a specific outcome as ‘good’ or ‘poor’ influences trial design, implying that personal value judgements affect the probability of a trial outcome to be positive or negative. Surgical decompression for space-occupying hemispheric infarction may serve as an example. If treated medically, patients with space-occupying hemispheric infarction have a poor prognosis with case fatality rates of approximately 80% [28]. Surgical decompression, through removal of a large part of the skull followed by a duraplasty, can normalize intracranial pressure and accommodate shifts of brain tissue, thereby preventing secondary damage or transtentorial herniation. In the 1990s, observational studies with historical controls have shown that surgical decompression reduces the high death rates with more than 50% [29,30]. Thereafter, this treatment has been advocated by experts with a strong belief in its benefit, despite the lack of evidence from randomized controlled trials. However, others were sceptical, as the large reduction in mortality by the operation probably came at the expense of an increase of severely disabled survivors, where they valued survival with neurological impairment leading to com805
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plete dependency lower than death. In the 2000s, three randomized clinical trials have been performed, testing the hypothesis that surgical decompression reduces mortality and improves functional outcome of patients with space-occupying hemispheric infarction. Two trials were designed by believers in the treatment’s benefit [31,32] whereas one was designed by sceptics [33]. In all the trials, the primary outcome measure was defined as death or disability as determined by the score on the modified Rankin Scale (mRS). Grades on the mRS range from 0 (no symptoms at all) to 5 (severe disability). Outcome was dichotomized as ‘favourable’ or ‘unfavourable’. The believing investigators of the first two trials defined a score of 0–4 as a favourable outcome. The sceptical investigators of the third trial valued a score of 4, indicating moderately severe disability and the inability to walk or attend to own bodily needs without assistance, as an unfavourable outcome. Through the classification of survival with severely impaired functional outcome as ‘good’, and the high prior probability of a reduction of mortality, the chance of finding positive trial results was higher for the first two trials as compared with the third. Results were accordingly: although distributions of outcomes were largely similar over the three trials, the first two concluded that surgical decompression increases the probability of a good functional outcome of patients with space-occupying hemispheric infarction [31,32], whereas the third showed no clinically relevant beneficial treatment effect [33]. This example illustrates that experts’ preferences or value judgements may have a critical impact on trial design and consequently on outcome. Because such value judgements are mostly left implicit, these influences are often hardly recognizable.
Interpretation of trial results depends on prior beliefs In EBM as a research tradition, reality is entirely phenomenological. Statements on the probability of occurrence of the various phenomena, for example, ‘relapsing stroke’, are based on arithmetic probability. For the inherent uncertainties, there is a frequent use of Bayesian statistics. Herewith, quantifications of uncertainties are obtained in the form of probability statements: a posterior probability is derived as a function of prior probability and the new data [34]. For example, in patients with ischaemic stroke and patent foramen ovale (PFO), a lower incidence of poor outcome in patients treated with oral anticoagulants as compared with those treated with platelet aggregation inhibitors in a subgroup analysis of a large randomized controlled trial has resulted in shifts of the assumed risk-benefit ratio of this treatment, with higher posterior than prior probabilities of benefit [35]. Another example is the updated meta-analysis on the effect of a combination of aspirin and dipyridamole as compared with aspirin alone for secondary prevention of vascular events after ischaemic stroke. Until 2006, meta-analyses of previous trials were inconclusive. Adding the results of the large, multicentre, randomized ESPRIT trial provided sufficient evidence to prefer the combination regimen [36]. In other words, Bayesian statistics are helpful in rationally redressing beliefs if new evidence is presented. Objective Bayesians seek an objective value for the prior probability. However, most hold that the prior probabilities represent subjective probabilities, also called degrees of belief. These degrees of belief are a function of the scientist’s belief in the proposition under research [37]. In medical sciences, subjective (prior) prob806
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abilities can be based on experts’ previous experiences: repeated observations in the past increase the subjective probability that a next observation will be likewise. As such, prior probabilities rather reflect how likely the scientist thinks a proposition is true than how likely this truth actually is. Thus, the use of prior probabilities implicitly justifies an expert’s inductive inferences. For example, in patients with cryptogenic ischaemic stroke and PFO, there is controversy about the usefulness of PFO closure to prevent stroke relapses [38]. Simplistically stated, cardiologists have a stronger belief in the treatment’s benefit than neurologists. If any, evidence for a causal relation between PFO and stroke comes from observational studies, mostly published in neurological journals, with various methodological weaknesses. For instance, in patients with cryptogenic stroke and PFO, other potential causes were often insufficiently excluded, and PFO was present not only in patients with brain infarcts but also in approximately 20% of healthy control subjects [39]. Therefore, neurologists are sceptical about cerebral infarcts being caused by PFO and consequently about any potential benefit of PFO closure. Otherwise, in cardiologic journals, a large number of observational studies have been published, suggesting that the rate of relapsing stroke was reduced after PFO closure [35]. Despite lack of control groups and unclear criteria for relapsing stroke in the majority, these studies have engendered cardiologists’ general belief in the benefit of PFO closure. If priors are indeed subjective, there is no objective standard for choosing between such conflicting hypotheses. Hence, Bayesian statistics can be used to rationally justify belief in any hypothesis, but this implies a rejection of objectivism. In 2012 and 2013, the benefit of PFO closure has been studied in three randomized controlled trials [40–42]. In all the three trials, there was a discretely lower incidence of poor outcome after PFO closure as compared with no closure. However, in none of the trials, this difference was statistically significant. These results have confirmed the relatively strong prior belief in the benefit of the treatment in the community of cardiologists, who claim that the lack of statistical significance is caused by heterogeneity of the patient groups and relatively short follow-up [43]. Otherwise, the same data, together with the low priors among neurologists, have led to even stronger neurological recommendations against PFO closure [44,45]. Accepting accumulated evidence as sufficient does not follow simple deductive rules. Rather, each step of the process, determining which evidence to include, defining the strength of the evidence and assessing generalizability, requires interpretive judgements [20]. There is no solution for conflicting results. If at all, only repeated application of Bayes’ theorem can lead to agreement on posterior probability.
Conclusion Expert opinion plays an important role in all essential steps of the pursuit of knowledge in the research tradition of EBM. It may be inferred that evidence from expert opinion differs in kind instead of in degree from evidence from randomized controlled trials. If so, these do not belong to the same quality ranking and should be defined as complementary rather than hierarchically ranked. Acknowledgement of prior beliefs and preferences may enhance interpretation and extrapolation of trial results and strengthen conclusions.
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Conflict of interest The author declares no conflict of interest.
Acknowledgements The author thanks Dr M. Boenink (philosopher), Professor Dr A. Algra (clinical epidemiologist) and Professor Dr ir. M.J.A.M. van Putten (neurologist) for critically reading and commenting for previous versions of the manuscript. Funding sources played no role in its preparation or in the decision to submit.
References 1. Guyatt, G. (1996) Evidence based medicine. Annals of Internal Medicine, 114 (Suppl. 2), A–16. 2. Sackett, D. L., Rosenberg, W. M., Gray, J. A., Haynes, R. B. & Richardson, W. S. (1996) Evidence based medicine: what it is and what it isn’t. BMJ (Clinical Research Ed.), 312, 71–72. 3. Davidoff, F., Haynes, B., Sackett, D. & Smith, R. (1995) Evidence based medicine. BMJ (Clinical Research Ed.), 310, 1085–1086. 4. Kuhn, T. S. (1961) The function of measurement in modern physical science. Isis; an International Review Devoted to the History of Science and Its Cultural Influences, 52, 161–193. 5. Kulkarni, A. V. (2005) The challenges of evidence-based medicine: a philosophical perspective. Medicine, Health Care, and Philosophy, 8, 255–260. 6. Evidence-Based Medicine Working Group (1992) Evidence-based medicine. A new approach to teaching the practice of medicine. JAMA: The Journal of the American Medical Association, 268, 2420– 2425. 7. Grayling, A. C. (1997) An Introduction to Philosophical Logic. Oxford: Blackwell publishing Ltd. 8. Wolff, H. & Gotzsche, P. (2000) Medicine and the Humanities. In Rational Diagnosis and Treatment, 3rd edn (eds H. Wulff & P. Gotzsche), pp. 147–174. Oxford: Blackwell Science. 9. Kuhn, T. S. (1970) The Structure of Scientific Revolutions, 3rd edn. London: University of Chicago press. 10. Popper, K. R. (1992) The Logic of Scientific Discovery. London: Routledge. 11. Aristotle (1920) Ethica Nicomachea. Oxford: Oxford University Press. 12. Popper, K. R. (2002) Conjectures and Refutations: The Growth of Scientific Knowledge. London: Routledge. 13. Batts, B. & Crawford, L. L. (1991) Problematic progress: a review of Laudan’s Progress and its problems and science and values. Journal of the Experimental Analysis of Behavior, 55, 337–349. 14. Kyriacou, D. N. (2004) Evidence-based medical decision making: deductive versus inductive logical thinking. Academic Emergency Medicine: Official Journal of the Society for Academic Emergency Medicine, 11, 670–671. 15. Anderton, B. H. (1987) Alzheimer’s disease. Progress in molecular pathology. Nature, 325, 658–659. 16. Castellani, R. J., Lee, H. G., Siedlak, S. L., Nunomura, A., Hayashi, T. & Nakamura, M. (2009) Reexamining Alzheimer’s disease: evidence for a protective role for amyloid-beta protein precursor and amyloidbeta. Journal of Alzheimer’s Disease, 18, 447–452. 17. Festinger, L., Riecken, W. & Schachter, S. (2008) When prophecy fails. London: Pinter & Martin Ltd. 18. Callaway, E. (2012) Alzheimer’s drugs take a new tack. Nature, 489, 13–14. 19. Longino, H. E. (1990) Science as Social Knowledge: Value and Objectivity in Scientific Inquiry. Princeton: Princeton University Press.
© 2014 John Wiley & Sons, Ltd.
Personal View
20. Feinstein, A. R. & Horwitz, R. I. (1997) Problems in the ‘evidence’ of ‘evidence-based medicine’. The American Journal of Medicine, 103, 529–535. 21. Schut, E. S., Brouwer, M. C., de Gans, J., Florquin, S., Troost, D. & van de Beek, D. (2009) Delayed cerebral thrombosis after initial good recovery from pneumococcal meningitis. Neurology, 73, 1988–1995. 22. Schut, E. S., Lucas, M. J., Brouwer, M. C., Vergouwen, M. D., van der Ende, A. & van de Beek, D. (2012) Cerebral infarction in adults with bacterial meningitis. Neurocritical Care, 16, 421–427. 23. Klein, M., Koedel, U., Kastenbauer, S., Pfister, H. W., van de Beek, D. & Scut, E. S. (2010) Delayed cerebral thrombosis after initial good recovery from pneumococcal meningitis: past as prologue: delayed stroke as a parainfectious process of bacterial meningitis? Neurology, 75, 193. 24. Vergouwen, M. D., Schut, E. S., Troost, D. & van de Beek, D. (2010) Diffuse cerebral intravascular coagulation and cerebral infarction in pneumococcal meningitis. Neurocritical Care, 13, 217–227. 25. Simonton, D. K. (1999) Darwinian Genius: The Future of An Idea. Origins of Genius Darwinian Perspectives on Creativity. Oxford: Oxford University Press. 26. Miller, C. (2009) The conditions of moral realism. Journal of Philosophical Research, 34, 123–155. 27. Strech, D. & Tilburt, J. (2008) Value judgments in the analysis and synthesis of evidence. Journal of Clinical Epidemiology, 61, 521–524. 28. Hacke, W., Schwab, S., Horn, M., Spranger, M., De Georgia, M. & von Kummer, R. (1996) ‘Malignant’ middle cerebral artery territory infarction: clinical course and prognostic signs. Archives of Neurology, 53, 309–315. 29. Rieke, K., Schwab, S. & Krieger, D. (1995) Decompressive surgery in space-occupying hemispheric infarction: results of an open, prospective trial. Critical Care Medicine, 23, 1576–1587. 30. Schwab, S., Steiner, T. & Aschoff, A. (1998) Early hemicraniectomy in patients with complete middle cerebral artery infarction. Stroke; a Journal of Cerebral Circulation, 29, 1888–1893. 31. Vahedi, K., Vicaut, E., Mateo, J., Kurtz, A., Orabi, M. & Guichard, J. P. (2007) Sequential-design, multicenter, randomized, controlled trial of early decompressive craniectomy in malignant middle cerebral artery infarction (DECIMAL Trial). Stroke; a Journal of Cerebral Circulation, 38, 2506–2517. 32. Juttler, E., Schwab, S., Schmiedek, P., Unterberg, A., Hennerici, M. & Woitzik, J. (2007) Decompressive Surgery for the Treatment of Malignant Infarction of the Middle Cerebral Artery (DESTINY): a randomized, controlled trial. Stroke; a Journal of Cerebral Circulation, 38, 2518–2525. 33. Hofmeijer, J., Kappelle, L. J., Algra, A., Amelink, G. J., van Gijn, J. & van der Worp, H. B. (2009) Surgical decompression for spaceoccupying cerebral infarction (the Hemicraniectomy After Middle cerebral artery infarction with Life-threatening Edema Trial [HAMLET]): a multicentre, open, randomised trial. Lancet Neurology, 8, 326–333. 34. Gurrin, L. C., Kurinczuk, J. J. & Burton, P. R. (2000) Bayesian statistics in medical research: an intuitive alternative to conventional data analysis. Journal of Evaluation in Clinical Practice, 6, 193–204. 35. Kitsios, G. D., Dahabreh, I. J., Abu Dabrh, A. M., Thaler, D. E. & Kent, D. M. (2012) Patent foramen ovale closure and medical treatments for secondary stroke prevention: a systematic review of observational and randomized evidence. Stroke; a Journal of Cerebral Circulation, 43, 422–431. 36. ESPRIT Study Group (2006) Aspirin plus dipyridamole versus aspirin alone after cerebral ischaemia of arterial origin (ESPRIT): randomised controlled trial. Lancet, 367, 1665–1673. 37. Jaynes, E. T. (1986) Annals of Internal Medicine, 114 (Suppl. 2), A–16. 38. Homma, S. & Di Tullio, M. R. (2010) Patent foramen ovale and stroke. Journal of Cardiology, 56, 134–141.
807
Personal View
39. Sacco, R. L., Ellenberg, J. H., Mohr, J. P., Tatemichi, T. K., Hier, D. B. & Price, T. R. (1989) Infarcts of undetermined cause: the NINCDS Stroke Data Bank. Annals of Neurology, 25, 382–390. 40. Carroll, J. D., Saver, J. L., Thaler, D. E., Smalling, R. W., Berry, S. & Mac Donald, L. A. (2013) Closure of patent foramen ovale versus medical therapy after cryptogenic stroke. The New England Journal of Medicine, 368, 1092–1100. 41. Furlan, A. J., Reisman, M., Massaro, J., Mauri, L., Adams, H. & Albers, G. W. (2012) Closure or medical therapy for cryptogenic stroke with patent foramen ovale. The New England Journal of Medicine, 366, 991–999. 42. Meier, B., Kalesan, B., Mattle, H. P., Khattab, A. A., Hildick-Smith, D. & Dudek, D. (2013) Percutaneous closure of patent foramen ovale in cryptogenic embolism. The New England Journal of Medicine, 368, 1083–1091.
808
J. Hofmeijer
43. Hakeem, A., Marmagkiolis, K., Hacioglu, Y., Uretsky, B. F., Gundogdu, B. & Leesar, M. (2013) Safety and efficacy of device closure for patent foramen ovale for secondary prevention of neurological events: Comprehensive systematic review and meta-analysis of randomized controlled trials. Cardiovascular Revascularization Medicine: Including Molecular Interventions, 14, 349–355. 44. Hofmeijer, J., Lindeboom, J. E. & Vermeer, S. E. (2014) Persistent foramen ovale and cerebral ischemia: treatment options? Tijdschrift voor Neurologie en Neurochirurgie, 114, 105–110. 45. Kappelle, L. J. (2013) Closure of patent foramen ovale after a TIA or minor ischaemic stroke; yet another logical, but unnecessary treatment. Nederlands Tijdschrift Voor Geneeskunde, 157, A6818.
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